Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer

Author:

Huber Marinus12ORCID,Kepesidis Kosmas V12ORCID,Voronina Liudmila1,Fleischmann Frank1,Fill Ernst2,Hermann Jacqueline1,Koch Ina3ORCID,Milger-Kneidinger Katrin4,Kolben Thomas5,Schulz Gerald B6,Jokisch Friedrich6,Behr Jürgen4,Harbeck Nadia5,Reiser Maximilian7,Stief Christian6,Krausz Ferenc12,Zigman Mihaela12ORCID

Affiliation:

1. Ludwig Maximilians University Munich (LMU), Department of Laser Physics

2. Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics

3. Asklepios Biobank for Lung Diseases, Department of Thoracic Surgery, Member of the German Center for Lung Research, DZL, Asklepios Fachkliniken München-Gauting

4. University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Internal Medicine V

5. University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Obstetrics and Gynecology, Breast Center and Comprehensive Cancer Center (CCLMU)

6. University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Urology

7. University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Clinical Radiology

Abstract

Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78–0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition.

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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